Low-resolution pedestrian detection method, system and storage medium combining resnet and senet

Abstract

The invention relates to image processing technology, in particular to a low-resolution pedestrian detection method, system and storage medium. The method of the present invention includes a training process and a testing process. The training process first determines the training set and the parameters of the training process; then inputs pictures in sequence according to the batch size, extracts the multi-scale features of the training pictures, and reconstructs and enhances the shallow features. Form a new multi-scale detection framework; finally perform frame classification and position regression, calculate training loss and backpropagation, and update weight parameters. The test process first determines the test set, uses the model obtained during the training process as the test model of the algorithm, sequentially inputs test pictures in small batches, extracts multi-scale features, reconstructs and enhances shallow features, and then classifies and positions frames return. The present invention uses a deep learning network to reconstruct shallow features, and at the same time improves the effectiveness of shallow features, so as to enhance the ability to detect low-resolution pedestrians.

Description

technical field [0001] The invention relates to image processing technology, in particular to a low-resolution pedestrian detection method, system and storage medium. Background technique [0002] Pedestrian detection is to judge whether there are pedestrians in the target image or video, and if so, it is necessary to accurately locate the detected pedestrians. As a very challenging research hotspot in computer vision, pedestrian detection has important applications in the fields of intelligent transportation, video surveillance, and robot development. Therefore, it has important research significance and great application value to study high-performance pedestrian detection system. [0003] In recent years, the popularity of deep learning has driven the development of pedestrian detection technology. Pedestrian detection has gradually shifted from traditional methods to deep learning methods. Deep learning methods have made great breakthroughs in detection speed and real-t...

AI Technical Summary

Problems solved by technology

[0013] The SSD algorithm directly uses the features extracted by the shallow network for classification and regression to detect low-resolution pedestrians. Since the effective features contained in low-resolution pedestrians are very limited, the features extracted by the shallow network may be interfered by various noises. This affects the detection of low-resolution pedestrians, so the effectiveness of the features extracted by the shallow network of the SSD algorithm cannot be guaranteed

Images